Comparative Analysis and Enhancement of Resource Allocation Technique in Kubernetes

Manoharan, Jeyasoorya (2024) Comparative Analysis and Enhancement of Resource Allocation Technique in Kubernetes. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (935kB) | Preview

Abstract

This research focuses on improving resource management strategies in Kubernetes which is used to manage containerized applications solving issues related to allocation, optimization, and scaling of their resources. Applying machine learning, the work along with the development of the new strategies, integrates the existing method of resource consumption prediction, resource utilization control, QoS-based task scheduling, and the novel hybrid auto scaling approach. Evaluation of performance metrics and the benchmarking process demonstrate the present state of resource management and its development tendencies for further optimization. The results reveal that linear regression models have a high level of accuracy in forecasting the CPU usage and, at the same time, the hybrid auto scaling strategy satisfactorily solves different constrains of resources. The QoS-aware scheduling algorithm analysis the improvement of the cluster efficiency and the responsiveness of the applications can be expected. Thus, these research results can be beneficial for both enhancing the theoretical knowledge of cloud computing and implementing the improved resource allocation methods in the Kubernetes clusters.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Emani, Sai UNSPECIFIED
Uncontrolled Keywords:	Kubernetes; Resource Allocation Mechanism; Comparative Study; Auto-Scaling; Quality of Service; containerization; cloud computing
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 08:23
Last Modified:	04 Jul 2025 08:23
URI:	https://norma.ncirl.ie/id/eprint/8035

Actions (login required)

View Item